Current Status of Photodynamic Diagnosis for Gastric Tumors

Fluorescence spectral analysis and logistic regression modeling for diagnosing basal cell carcinoma on head and neck

The optical fluorescence method is distinguished by key features such as non-invasiveness, high sensitivity, and resolution, which are superior to traditional diagnostic approaches. Unlike histopathological examinations and biochemical analyses, optical diagnostic methods obviate the need for tissue sampling, enabling the analysis of virtually unlimited material. The research aims to examine the effectiveness of emission spectra analysis in the diagnosis of basal cell carcinoma (BCC) of the scalp and neck. The analysis was based on data provided by Specialized Hospital No. 2 in Bytom comprising a study sample of 10 patients. For each patient, fluorescence emission spectra were recorded from each of 512 points along a 5 mm line. The results obtained from the histopathological examination, the analysis and morphological evaluation of the tissue, and the diagnosis through microscopic observation were used to define a dichotomous variable (presence or absence of a cancerous lesion), adopted in the study as the modeled variable. The next step of the presented study involved constructing a logistic regression model for identifying cancerous lesions depending on the biochemical indicator's relative fluorescence value (RFV) and emission wavelength (ELW) within the 620 nm to 730 nm range. This wavelength range is often used in fluorescence diagnostics to detect various pathologies, including cancerous lesions. The resulting binary logistic regression model, logit(p)=-33.17+0.04ELW+0.01RFV, indicates a statistically significant relationship between wavelength and relative fluorescence values with the probability of detecting cancer. The estimated model exhibits a good fit and high predictive accuracy. The overall model accuracy is 84.8 %, with the correct classification rates at approximately 96 % for healthy individuals and 74 % for individuals with cancer. These findings underscore the potential of photodynamic diagnostics in cancer detection and monitoring.

A New Approach for Gastrointestinal Tract Findings Detection and Classification: Deep Learning-Based Hybrid Stacking Ensemble Models

Endoscopic procedures for diagnosing gastrointestinal tract findings depend on specialist experience and inter-observer variability. This variability can cause minor lesions to be missed and prevent early diagnosis. In this study, deep learning-based hybrid stacking ensemble modeling has been proposed for detecting and classifying gastrointestinal system findings, aiming at early diagnosis with high accuracy and sensitive measurements and saving workload to help the specialist and objectivity in endoscopic diagnosis. In the first level of the proposed bi-level stacking ensemble approach, predictions are obtained by applying 5-fold cross-validation to three new CNN models. A machine learning classifier selected at the second level is trained according to the obtained predictions, and the final classification result is reached. The performances of the stacking models were compared with the performances of the deep learning models, and McNemar's statistical test was applied to support the results. According to the experimental results, stacking ensemble models performed with a significant difference with 98.42% ACC and 98.19% MCC in the KvasirV2 dataset and 98.53% ACC and 98.39% MCC in the HyperKvasir dataset. This study is the first to offer a new learning-oriented approach that efficiently evaluates CNN features and provides objective and reliable results with statistical testing compared to state-of-the-art studies on the subject. The proposed approach improves the performance of deep learning models and outperforms the state-of-the-art studies in the literature.

Research trends in endoscopic applications in early gastric cancer: A bibliometric analysis of studies published from 2012 to 2022

Background

Endoscopy is the optimal method of diagnosing and treating early gastric cancer (EGC), and it is therefore important to keep up with the rapid development of endoscopic applications in EGC. This study utilized bibliometric analysis to describe the development, current research progress, hotspots, and emerging trends in this field.

Methods

We retrieved publications about endoscopic applications in EGC from 2012 to 2022 from Web of Science™ (Clarivate™, Philadelphia, PA, USA) Core Collection (WoSCC). We mainly used CiteSpace (version 6.1.R3) and VOSviewer (version 1.6.18) to perform the collaboration network analysis, co-cited analysis, co-occurrence analysis, cluster analysis, and burst detection.

Results

A total of 1,333 publications were included. Overall, both the number of publications and the average number of citations per document per year increased annually. Among the 52 countries/regions that were included, Japan contributed the most in terms of publications, citations, and H-index, followed by the Republic of Korea and China. The National Cancer Center, based in both Japan and the Republic of Korea, ranked first among institutions in terms of number of publications, citation impact, and the average number of citations. Yong Chan Lee was the most productive author, and Ichiro Oda had the highest citation impact. In terms of cited authors, Gotoda Takuji had both the highest citation impact and the highest centrality. Among journals, Surgical Endoscopy and Other Interventional Techniques had the most publications, and Gastric Cancer had the highest citation impact and H-index. Among all publications and cited references, a paper by Smyth E C et al., followed by one by Gotoda T et al., had the highest citation impact. Using keywords co-occurrence and cluster analysis, 1,652 author keywords were categorized into 26 clusters, and we then divided the clusters into six groups. The largest and newest clusters were endoscopic submucosal dissection and artificial intelligence (AI), respectively.

Conclusions

Over the last decade, research into endoscopic applications in EGC has gradually increased. Japan and the Republic of Korea have contributed the most, but research in this field in China, from an initially low base, is developing at a striking speed. However, a lack of collaboration among countries, institutions, and authors, is common, and this should be addressed in future. The main focus of research in this field (i.e., the largest cluster) is endoscopic submucosal dissection, and the topic at the frontier (i.e., the newest cluster) is AI. Future research should focus on the application of AI in endoscopy, and its implications for the clinical diagnosis and treatment of EGC.

Current status and future perspective of linked color imaging for gastric cancer screening: a literature review

Screening endoscopy has advanced to facilitate improvements in the detection and prognosis of gastric cancer. However, most early gastric cancers (EGCs) have subtle morphological or color features that are difficult to detect by white-light imaging (WLI); thus, even well-trained endoscopists can miss EGC when using this conventional endoscopic approach. This review summarizes the current and future status of linked color imaging (LCI), a new image-enhancing endoscopy (IEE) method, for gastric screening. LCI has been shown to produce bright images even at a distant view and provide excellent visibility of gastric cancer due to high color contrast relative to the surrounding tissue. LCI delineates EGC as orange-red and intestinal metaplasia as purple, regardless of a history of Helicobacter pylori (Hp) eradication, and contributes to the detection of superficial EGC. Moreover, LCI assists in the determination of Hp infection status, which is closely related to the risk of developing gastric cancer. Transnasal endoscopy (ultra-thin) using LCI is also useful for identifying gastric neoplastic lesions. Recently, several prospective studies have demonstrated that LCI has a higher detection ratio for gastric cancer than WLI. We believe that LCI should be used in routine upper gastrointestinal endoscopies.

Gastric Cancer Screening in Japan: A Narrative Review

Gastric cancer is the second leading cause of cancer incidence in Japan, although gastric cancer mortality has decreased over the past few decades. This decrease is attributed to a decline in the prevalence of H. pylori infection. Radiographic examination has long been performed as the only method of gastric screening with evidence of reduction in mortality in the past. The revised 2014 Japanese Guidelines for Gastric Cancer Screening approved gastric endoscopy for use in population-based screening, together with radiography. While endoscopic gastric cancer screening has begun, there are some problems associated with its implementation, including endoscopic capacity, equal access, and cost-effectiveness. As H. pylori infection and atrophic gastritis are well-known risk factors for gastric cancer, a different screening method might be considered, depending on its association with the individual’s background and gastric cancer risk. In this review, we summarize the current status and problems of gastric cancer screening in Japan. We also introduce and discuss the results of gastric cancer screening using H. pylori infection status in Hoki-cho, Tottori prefecture. Further, we review risk stratification as a system for improving gastric cancer screening in the future.

Photodynamic Diagnosis for the Identification of Intestinal-Type Gastric Cancers and High-Grade Adenomas

Gastric cancer is the second most common cancer in Japan. The incidence of gastric cancer remains high owing to the increase in the elderly population. Endoscopy outperforms radiography in identifying early gastric cancer (EGC). Furthermore, image-enhanced endoscopy (IEE) has been developed and implemented worldwide in clinical practice. Magnifying IEE images can help to visualize the microvascular pattern and microstructure architecture, which is used for the characterization of EGC. However, accurate endoscopic diagnosis requires the experience and skill of endoscopists, making an objective and simple diagnostic method desirable. In this retrospective study, we investigated the diagnostic yield of 5-aminolevulinic acid (5-ALA)-mediated photodynamic diagnosis (PDD) for identifying gastric cancers and high-grade adenomas. In total, 52 lesions from 43 patients were ultimately included in the study. We detected 5-ALA-mediated protoporphyrin IX fluorescence in 45 of the 52 lesions that were initially intended for PDD, resulting in a detection rate of 86.5%, whereas each signet ring cell carcinoma was negative using 5-ALA PDD. In eight of the patients with multiple lesions, 17 lesions were identified using 5-ALA PDD. Again, we took biopsies from six areas that we suspected as new lesions. While 4 lesions were gastric neoplasms resected by endoscopic submucosal dissection, two other lesions were normal. Preoperative 5-ALA-PDD could provide additional diagnostic yields to detect such multiple lesions simultaneously. No severe adverse events were observed. Prospective multicenter studies are warranted to confirm the usefulness of 5-ALA PDD for EGC identification.